SG: Exploring the Impact of Model (Mis) Specification on Empirical Divergence-Time Estimates

系统发生树——对物种间进化关系的估计——已经成为系统生物学、进化生物学、生态学、分子生物学和流行病学等几乎所有研究领域的核心，因为它们提供了重要而明确的历史视角。系统发育学已经将其影响的分支从科学领域扩展到公共领域，为有关病原体的监测和监测、疫苗设计和保护重点的决策提供了信息。虽然许多系统发育是基于从现存物种（或菌株）收集的分子序列数据（DNA），但这些树也可以提供关于谱系内绝对或相对分支时间的信息。这种时间信息对于许多问题都是至关重要的，比如推断一种病毒的毒性毒株何时首次出现，以及估计它的变化速度有多快。这些考虑推动了大量数学模型的发展，用于推断进化树的时间尺度。该项目旨在利用经验数据集评估这些数学模型的可靠性，并将告知研究人员和公众使用这些方法的最佳实践。通过加州大学历史黑人学院和大学计划和美国戴维斯计划招收的本科生将接受统计系统发育方法和生物信息学方面的培训。所有开发的软件都将在开源许可下自由发布。研究人员还将开发一个新的，独立的研讨会和相关的教材，用于贝叶斯发散时间估计方法，包括不直接从事系统发育研究的研究人员。本研究的主要目的是探索贝叶斯方法在经验设置中估计物种分化时间的统计行为。这一主要目标将通过将所有目前实现的松弛时钟模型和校准方法应用于大量经验数据集样本来实现：(1)揭示散度时间估计对指定的松弛时钟模型/校准方法的敏感程度；(2)探讨了三个主要模型成分——分支率先验、节点年龄先验和校准方法——对发散时间估计的相对影响；(3)利用鲁棒贝叶斯模型比较方法评估候选松弛时钟模型池与实际数据的相对拟合；(4)开发分析协议并实现自动化高效探索松弛时钟模型空间进行实证分析的管道。促进更仔细的模型选择将提高我们估计分歧时间的能力，这反过来将广泛地造福于科学和更广泛的社区。

Phylogenetic trees - estimates of the evolutionary relationships among species - have become central to virtually all areas of research in systematics, evolutionary biology, ecology, molecular biology, and epidemiology because of the essential and explicit historical perspectives they provide. Phylogenies have extended the branches of their influence from the scientific to the public realm, informing decisions regarding the surveillance and monitoring of pathogens, vaccine design, and conservation priorities. Although many phylogenies are based on molecular sequence data (DNA) collected from extant species (or strains), these trees can also provide information regarding the absolute or relative branching times within a lineage. This temporal information is critical to many questions, such as inferring when a virulent strain of a virus first arose and estimating how quickly it is changing. These considerations have motivated the development of a large number of mathematical models for inferring the time scale of evolutionary trees. This project seeks to assess the reliability of these mathematical models using empirical datasets, and will inform researchers and public alike on the best practices for using these methods. Undergraduates recruited through the University of California Historically Black Colleges and Universities initiative and the US Davis Initiative for Maximizing Student Diversity will be trained in statistical phylogenetic methods and bioinformatics. All software developed will be distributed freely under open-source licenses. The researcher will also develop a new, stand-alone workshop and associated teaching materials on Bayesian divergence-time estimation methods to be used broadly, including by researchers who do not work directly in phylogenetic research.The primary objective of this research is to explore the statistical behavior of Bayesian methods for estimating species divergence times in an empirical setting. This main goal will be achieved by applying all currently implemented relaxed-clock models and calibration methods to a large sample of empirical datasets to: (1) reveal the extent to which divergence-time estimates are sensitive to the specified relaxed-clock model/calibration method; (2) explore the relative influence of the three primary model components - branch-rate priors, node-age priors, and calibration approaches - on divergence-time estimates; (3) assess the relative fit of the pool of candidate relaxed-clock models to real data using robust Bayesian model-comparison methods; and (4) develop analytical protocols and implement pipelines that automate the efficient exploration of relaxed-clock model space for empirical analyses. Facilitating more careful model selection will improve our ability to estimate divergence times, which, in turn, will broadly benefit scientific and broader communities.